Traffic generator with enhanced burst modeling feature

ABSTRACT

A traffic generator is disclosed which generates a first type of traffic in accordance with a given distribution, and generates a second type of traffic that includes at least one traffic burst. The traffic burst is generated based at least in part on an amount of the first type of traffic generated over one or more time intervals. For example, in an illustrative embodiment, generation of the second type of traffic involves accumulating traffic over one or more of the time intervals for which the first type of traffic is generated, and generating the traffic burst based at least in part on the accumulated traffic.

RELATED APPLICATION(S)

The present invention is related to the invention described in U.S.Patent Application Attorney Docket D 1-4-2-1-3, entitled “ExtensibleTraffic Generator for Synthesis of Network Data Traffic,” which is filedconcurrently herewith and incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to communication systems, andmore particularly to techniques for generating data traffic for use intesting or other processing applications in such systems.

BACKGROUND OF THE INVENTION

Traffic generators are commonly utilized in generating data traffichaving characteristics suitable for testing a given communication systemdesign. Such traffic generators may be implemented in hardware orsoftware. Data traffic characteristics such as the time distribution ofpacket arrival are critical for testing communication system performanceparameters such as buffering and scheduling capacity. It is generallydesirable for the traffic generator to provide data traffic output whichclosely models the “real-life” behavior of packet arrival timing in thesystem. For example, such behavior often involves so-called burstarrival, when a certain number of packets arrive substantiallyback-to-back, that is, one after another without any significantintervening time between arriving packets.

In order to provide proper stress testing of the components of acommunication system, in a system design phase or otherwise, a trafficgenerator should incorporate an efficient and accurate burst model.Unfortunately, conventional traffic generators typically utilize bursttechniques, such as constant burst or probabilistic burst, that fail toprovide adequate levels of efficiency and accuracy. As a result, suchtraffic generators do not provide sufficiently close modeling of“real-life” packet arrival behavior in a communication system.

Although other burst modeling techniques are known in the context ofqueuing theory, such techniques are often not readily applicable for usein practical hardware or software traffic generators. One such techniqueis the Hurst parameter, which has been used to describe burst behaviorin theoretical network traffic description as well as in predictingnatural burst events such as floods. Additional details can be found in,for example, W. Stallings, “High Speed Networks and Internets:Performance and Quality of Service,” Chapter 9, and W. E. Leland, “Onthe Self-Similar Nature of Ethernet Traffic,” IEEE/ACM Transactions onNetworking, February 1994. However, the Hurst parameter ismathematically very complex, and therefore difficult to understand andformulate. In addition, it exhibits a computational complexity whichmakes it highly impractical to implement in a hardware or softwaretraffic generator.

Accordingly, a need exists in the art for a traffic generator whichprovides improved modeling of burst arrival, in a manner that overcomesthe disadvantages of the conventional techniques noted above.

SUMMARY OF THE INVENTION

The present invention provides a traffic generator having an enhancedburst modeling feature based on what is referred to herein as acompensatory burst model.

In accordance with one aspect of the invention, a traffic generatorgenerates a first type of traffic in accordance with a givendistribution, and generates a second type of traffic that includes atleast one traffic burst. The traffic burst is generated based at leastin part on an amount of the first type of traffic generated over one ormore time intervals.

By way of example, in an illustrative embodiment, generation of thesecond type of traffic involves accumulating traffic over one or more ofthe time intervals for which the first type of traffic is generated, andgenerating the traffic burst based at least in part on the accumulatedtraffic. More specifically, the second type of traffic may comprise aplurality of traffic bursts, with a given one of the traffic burstsbeing generated by determining a current burst size and a currentcompensatory-accumulation size, creating an initially-empty burstcontainer having a capacity that is equal to the burst size, addingcompensatory traffic to the burst container whenever the total trafficof the first type generated within a given sample slot time is less thana comparison level, such that for each such addition of compensatorytraffic, a level of traffic in the burst container increases by thecompensatory-accumulation size, and generating the given traffic burstwhen the burst container level is greater than or equal to the burstsize.

Advantageously, the compensatory burst model generates traffic bursts ina manner which tends to compensate for temporary reductions in theamount of traffic of the first type, so as to substantially maintain adesired level of traffic flow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative embodiment of a traffic generatorconfigured in accordance with the invention.

FIGS. 2A and 2B illustrate the determination of burst size andcompensatory-accumulation size, respectively, in a traffic generationprocess in the traffic generator of FIG. 1.

FIG. 3 shows an example traffic generation process implemented in thetraffic generator of FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will be illustrated herein using an exemplarytraffic generator providing an enhanced burst modeling feature. Itshould be understood, however, that the techniques of the invention canbe more generally applied to any type of traffic generation application.The invention does not require the particular elements of theillustrative embodiment, and other elements can be used in addition toor in place of the particular elements shown.

FIG. 1 shows an illustrative embodiment of a traffic generator 100configured in accordance with the invention. The traffic generator 100includes a traffic manager 102, a traffic file memory 104, and an outputinterface bus 106.

The traffic manager 102 manages the traffic generation functions of thetraffic generator 100, and includes in this embodiment a timestampgenerator 110, a timestamp manager 112, a protocol data unit (PDU)generator 114, a traffic supplier 116, and an event subsystem and/orforwarder 118. Generated traffic patterns, parameters and othertraffic-related information are stored in the traffic file memory 104.Traffic generated by the traffic generator 100 under the control of thetraffic manager 102 may be delivered via output interface bus 106 to oneor more devices.

In generating traffic using the techniques described herein, the trafficmanager 102 may also utilize distribution models 120, traffic models122, protocol models 124, and configurable elements 126. Examples ofconfigurable elements include a pattern generator 130 and a sequencer132.

Additional details regarding the operation of one or more of the modulesor other elements of the traffic generator 100 may be found in theabove-cited U.S. Patent Application Attorney Docket Attorney Docket D1-4-2-1-3.

The traffic generator 100 or other traffic generator configured inaccordance with the invention can be implemented in hardware, software,firmware or any combination of these. For example, the traffic generator100 may be viewed as a hardware traffic generator or a software trafficgenerator, or a combination of both. Generally, the traffic generator100, whether implemented as a hardware traffic generator, a softwaretraffic generator or a combination of both, may be implemented as orwithin an information processing device having at least one processorand at least one associated memory.

A more particular example of a software traffic generator is a softwaretraffic generator which comprises an element of a software-baseddevelopment tool for simulating the operation of interconnectedintegrated circuits or other types of electronic systems. Such asoftware-based development tool is typically configured to run at leastin part on a general-purpose computer, workstation or other informationprocessing device comprising at least one processor and an associatedmemory.

The traffic generator 100 is configured to execute one or more trafficgeneration processes, as will be described in greater detail below inconjunction with FIGS. 2 and 3. Such processes are implemented at leastin part in the form of software comprising one or more programs storedin the memory of the information processing device and executed by theprocessor of the information processing device. The configuration andoperation of such information processing devices are well-known in theart, and therefore not described in further detail herein.

It is also to be appreciated that the traffic generator 100 in theillustrative embodiment may further include, in addition to or in placeof the particular modules or other elements shown in FIG. 1, additionalmodules and other elements of a type commonly found in conventionalimplementations of such traffic generators. These conventional modulesand other elements, being commonly used in the art, will not bedescribed in detail herein.

The present invention in accordance with one aspect thereof providesimproved techniques for burst modeling in a traffic generator. Thetechniques can be utilized in generating packet bursts or other burstsof data traffic. For example, the techniques can be used to model thebehavior of file transfer protocol (FTP) traffic experienced by anetwork server in a network-based communication system, and in numerousother traffic modeling applications.

The particular burst model utilized in the illustrative embodiment maybe stored in the set of traffic models 122, and utilized by the trafficmanager 102 to generate the corresponding traffic in the mannerdescribed herein.

It is to be appreciated that, although described in the context ofnetwork traffic arrival, the burst modeling techniques of the inventioncan be applied to a wide variety of other traffic generationapplications. Also, the invention does not require the use ofpacket-based traffic or any other particular data traffic format.

In a generalized queuing system, inter-arrival time between twoconsecutive queue elements follows a certain probabilistic distribution,which may be used to model the operation of the system. Similarly, thedata traffic that arrives at a given node in a network-basedcommunication system may also be observed to follow a probabilisticdistribution taken from queuing theory. An example of one suchprobabilistic distribution is the well-known Poisson distribution.Assuming that the sequence of inter-arrival times between consecutivequeue elements is represented by {A1, A2, A3. . . Ak}, then the arrivalprocess in the corresponding queuing system is said to follow a Poissondistribution if the following holds:

-   -   1. Arrivals occur one at a time.    -   2. The distribution of the number of arrivals between time t and        time t+s depends only on the length of the interval s and not on        the starting time t.    -   3. The variable that represent the interarrival time, i.e., the        variable that takes one value from the sequence {A1, A2, A3. . .        Ak}, is an independent random variable.    -   4. The probability that Ai, i=1, 2, . . . k, is less than or        equal to t, is given by (1−exp(−λt)), where λ is the average        rate of arrivals per unit time.

Burst arrival occurs in the generalized queuing system when there is notime difference between arrivals of elements of the queue. As indicatedpreviously, in the data traffic context, burst arrivals generallycorrespond to back-to-back packet arrivals. Conventional techniques areunable to provide efficient and accurate modeling of such a condition,in a manner suitable for use in a hardware or software trafficgenerator.

The illustrative embodiment of the present invention provides animproved burst model referred to herein as the compensatory burst model.It should be noted that the compensatory burst model may be combinedwith other models that generate non-burst arrivals in order to achieve adesired traffic arrival pattern.

Utilizing the compensatory burst model of the illustrative embodiment,burst traffic may be viewed as being dependent at least in part on thenormal course of packet arrivals. For example, if a certain timeinterval witnesses a shortage of packet arrivals, packet bursts may beviewed as eventually compensating the shortage, such that a particularlevel of packet flow is maintained over a longer period of time.

The compensatory burst model will now be described in greater detailwith reference to the diagrams of FIGS. 2 and 3. The compensatory burstmodel is based at least in part on accumulating traffic over a givenqueuing process. More specifically, the model involves generating andmonitoring a particular pattern of traffic referred to herein as“comparative traffic.” Such traffic is generated in accordance with aspecified normal traffic arrival process. When the total amount ofcomparative traffic that is generated within a specified time interval,referred to herein as a sample slot time, falls below a specifiedcomparison level, a certain amount of burst traffic is accumulated in aburst container. The burst occurs when the total amount of accumulatedburst traffic in the burst container reaches a specified burst size.

The term “burst container” as used herein is intended to include, by wayof example and without limitation, a counter or other set of informationstored in a specified location in a memory of an information processingdevice. The process of accumulating burst traffic by adding traffic to aburst container may be implemented by incrementing a counter or otherset of information which characterizes the contents of a burstcontainer. It should therefore be understood that references in thedescription of the illustrative embodiment to the accumulation of bursttraffic or the addition of traffic to a container do not require the useof actual traffic per se, but can instead be implemented in astraightforward manner using conventional counters, data structuresand/or associated processing logic.

In the illustrative embodiment, the comparative traffic may be generatedso as to follow the above-described Poisson distribution. Other types ofdistributions may be used for generating the comparative traffic,including by way of example a Gaussian distribution or any other desireddistribution, as will be readily appreciated by those skilled in theart. The comparative traffic may thus be generated using a givendistribution which is itself a combination of multiple distributions.

It will be assumed for purposes of illustration that the compensatoryburst model operates using a number of user-defined parameters. Theseparameters may include the following:

-   -   1. General information such as physical line capacity, total        number of packets, etc.    -   2. The normal traffic arrival process to be taken as the        comparative traffic.    -   3. The comparison level.    -   4. The mean burst size and its variation range.    -   5. The mean compensatory-accumulation size and its variation        range.        These user-defined parameters are advantageous in terms of        providing an exceptionally high degree of user control over        burst generation. It should be understood, however, that the        invention does not require the use of these particular        parameters, nor does it require that, if used, any of these        parameters be user-defined. For example, one or more of these        parameters may be predetermined, automatically computed, or        determined using other techniques.

With reference to FIG. 2A, it can be seen that the mean burst size andits specified variation range determine the current burst size for eachburst. For each burst, the above-noted burst container has a capacityequal to the current burst size.

Similarly, FIG. 2B shows that the mean compensatory-accumulation sizeand its specified variation range determine thecompensatory-accumulation size for each burst.

This compensatory-accumulation size is also referred to herein as a“mug” size, in that the compensatory burst model may be viewed asfilling a given burst container 300 with a mug 302, as indicated in thediagram of FIG. 3. The burst container is also referred to herein as a“bucket” and has a capacity corresponding to the current burst size.Clock signals 305 used to control traffic generation are also shown inthe diagram of FIG. 3.

With continued reference to FIG. 3, the graphical plot portion of thediagram indicated generally by 310 plots the total traffic generated ineach of a plurality of sample slot times as a function of time. Asindicated previously, the traffic may be generated utilizing clocksignals 305. A comparison level 312 corresponding to a constant amountof traffic is also shown. The total traffic generated in a given sampleslot time corresponds generally to one of the vertical lines plotted inthe graphical plot portion 310. For each sample slot time for which thetotal traffic generated is less than the comparison level, the level ofthe burst container 300 is increased by the addition of an amount oftraffic equal to the compensatory-accumulation size. As indicatedpreviously, this is illustrated by the emptying of the contents of mug302 into the burst container 300 as shown.

The sample slot times denoted generally by the solid circles 320, 322,324, 326 and 328 denote sample slot times for which the amount ofgenerated traffic is less than the comparison level, with each suchsample slot time resulting in an increase in the contents of the burstcontainer by an amount corresponding to the compensatory-accumulationsize. The sample slot time denoted generally by the dashed circle 330represents the sample time slot for which the burst container levelbecomes greater than or equal to the current burst size. The bursttherefore occurs substantially at this point in time. The process ofaccumulating burst traffic in the burst container and subsequentlygenerating a burst when the burst container level equals or exceeds theburst size then repeats itself indefinitely, for as long as thisparticular burst modeling is desired.

A given interval between two consecutive bursts in the illustrativeembodiment is always a multiple of the sample slot time. A longer sampleslot time will therefore result in longer intervals between consecutivebursts.

Other parameters, such as the comparison level,compensatory-accumulation size and burst size, also influence theinterval between consecutive bursts. For example, the higher thecomparison level, the longer the interval between consecutive bursts.Similarly, the larger the burst size, the longer the interval betweenconsecutive bursts, and the larger the generated bursts. With regard tothe compensatory-accumulation size, an increase in this size willshorten the interval between consecutive bursts, and vice-versa.

A more detailed example of a traffic generation process utilizing thecompensatory burst model of the invention will now be described. It willbe assumed that the comparative-traffic distribution is selected as aPoisson distribution, and that a fixed sample slot time and a fixedcomparison level are used. The time instance at which a given trafficburst is generated is denoted in this example as t_(b). The trafficgeneration process with compensatory burst modeling proceeds as follows.

At Time Instance (0):

-   -   Burst size is determined.    -   Compensatory-accumulation size is determined.    -   An empty burst container is created having a capacity that is        equal to the burst size.        At Time Instance (0+Sample Slot Time):

The generated comparative traffic is compared with the comparison level.If the generated comparative traffic is less than the comparison level,an amount of compensatory traffic equal to the compensatory-accumulationsize is added to the empty burst container, so that the current burstcontainer level increases by the compensatory-accumulation size. Nocompensatory traffic is added to this burst container if the generatedcomparative traffic is greater than or equal to the comparison level.After addition of compensatory traffic to the burst container, theaccumulation of compensatory traffic in the burst container is comparedwith the burst size. If the accumulated compensatory traffic is greaterthan or equal to the burst size, the burst is generated at a burst timet_(b) which corresponds substantially to time instance (0+sample slottime). It will be assumed for this example that the result of thiscomparison of accumulated compensatory traffic with burst size indicatesthat the accumulated compensatory traffic is less than the burst size.

At Each of One or More Subsequent Time Instances (0+2*(Sample SlotTime)). (0+(n−1)* (Sample Slot Time)):

The total comparative traffic generated between the current timeinstance and the immediately preceding time instance is compared withthe comparison level. If the generated comparative traffic is less thanthe comparison level, an amount of compensatory traffic equal to thecompensatory-accumulation size is added to the burst container, so thatthe current burst container level increases by thecompensatory-accumulation size. No additional compensatory traffic isadded to the burst container if the generated comparative traffic isgreater than or equal to the comparison level. After addition ofcompensatory traffic to the burst container, the accumulation ofcompensatory traffic in the burst container is compared with the burstsize. If the accumulated compensatory traffic is greater than or equalto the burst size, the burst is generated at a burst time t_(b) whichcorresponds substantially to the current time instance. It will beassumed for this example that the result of this comparison ofaccumulated compensatory traffic with burst size indicates that theaccumulated compensatory traffic is less than the burst size for each ofthe time instances (0+2*(sample slot time)), . . . (0+(n−1)*(sample slottime)).

At Time Instance (0+n*(Sample Slot Time)):

The total comparative traffic generated between the time instance(0+(n−1)*(sample slot time)) and the time instant (0+n*(sample slottime)) is compared with the comparison level. If the generatedcomparative traffic is less than the comparison level, an amount ofcompensatory traffic equal to the compensatory-accumulation size isadded to the burst container, so that the current burst container levelincreases by the compensatory-accumulation size. No additionalcompensatory traffic is added to the burst container if the generatedcomparative traffic is greater than or equal to the comparison level.After addition of compensatory traffic to the burst container, theaccumulation of compensatory traffic in the burst container is comparedwith the burst size. If the accumulated compensatory traffic is greaterthan or equal to the burst size, the burst is generated at a burst timet_(b) which corresponds substantially to the time instance (0+n*(sampleslot time)). It will be assumed for this example that the result of thiscomparison of accumulated compensatory traffic with burst size indicatesthat the accumulated compensatory traffic is greater than or equal tothe burst size for time instance (0+n*(sample slot time)), such that theburst is generated substantially at this point in time.

At Time Instance (0+(n+1)*(Sample Slot Time)):

-   -   New burst size is determined.    -   New compensatory-accumulation size is determined.    -   An empty burst container is created having a capacity that is        equal to the burst size.    -   Future reference burst and compensatory-accumulation sizes may        be updated to reflect the new values.    -   The burst accumulation then proceeds in a similar manner, and        whenever the burst container is filled with accumulated        compensatory traffic, the corresponding burst occurs.

The foregoing traffic generation example can be generally viewed ascomprising the following steps for each burst to be generated:

-   -   1. A current burst size and a compensatory-accumulation size are        determined.    -   2. A burst container having a capacity that is equal to the        burst size is created, and is initially empty.    -   3. Compensatory traffic is added to the burst container whenever        the total traffic generated within a given sample slot time is        less than the comparison level. For each such addition of        compensatory traffic, the level of traffic in the burst        container increases by the compensatory-accumulation size.    -   4. Whenever the burst container level is greater than or equal        to the burst size, the burst occurs.

A given burst in this example is thus accumulated over a comparativetraffic process. During the burst, the comparative traffic process ispreferably halted, but such halting is not a requirement of theinvention.

An example set of parameters for use in the illustrative embodiment ofthe invention is as follows.

-   -   1. The line speed is OC-48, or 2.488320000 Gbps.    -   2. The average usage is 72% or 1.791590400 Gbps. This is the        mean value of generation for the comparative traffic.    -   3. The sample slot time is 0.000034 second, or 34 microseconds.    -   4. The comparison level may be on the order of the product of        the comparative traffic mean value and the sample slot time,        that is, 1791590400 bits/second x 0.000034 second, or 60914        bits. If the comparison level is increased, for example, to        62000 bits, the comparative traffic will fall short of the level        more frequently so that accumulation will occur more frequently,        and the inter-arrival time of the bursts will be reduced.        Similarly, if one were to use a value of 60000 bits, which is        lower than the product of the mean value and the sample slot        time, accumulation will occur less frequently than if the 60914        value were used.    -   5. The mean burst size is 5000 packets.    -   6. The variation in burst size is 560 packets, so that the burst        size can vary from 5000+560 packets to 5000-560 packets. Each        packet may have a length varying from 200 bytes to 300 bytes.    -   7. The mean compensatory-accumulation size is 700 packets and        the associated variation is 140 packets, so that the range of        compensatory-accumulation sizes is 700+140 packets to 700-140        packets.

It should be understood that these particular parameter values areprovided solely by way of example. The invention does not require theuse of these values or any other particular parameter values.

The combination of the comparative traffic and the traffic burstsadvantageously provides improved modeling of “real-life” trafficbehavior. More specifically, with regard to the present example, thetraffic arrival primarily follows a Poisson distribution, with theperiodic bursts tending to compensate the temporary loss in normaltraffic arrival, such that a particular level of traffic flow ismaintained over a longer period of time.

The present invention in the illustrative embodiment described aboveovercomes one or more of the drawbacks of the conventional techniques.For example, a traffic generator with an enhanced burst modeling featurein accordance with the invention provides improved efficiency andaccuracy in modeling of “real-life” traffic behavior in a network-basedcommunication system. A high degree of flexibility and user control inthe burst generation process is provided. Also, the invention can bereadily implemented in a practical hardware or software trafficgenerator. The invention allows a wide variety of burst-relatedprocessing applications, such as the benchmarking of communicationsystems against burst behavior, to be implemented in an efficientmanner.

As mentioned previously, one or more software programs for implementingthe traffic generation functionality described herein may be stored in amemory of an information processing device and executed by a processorof that device.

It should again be emphasized that the above-described embodiment isintended to be illustrative only. For example, alternative embodimentsmay be configured which utilize different traffic generatorconfigurations, modeling parameters, parameter values, or processingsteps than those specifically described herein.

These and numerous other alternative embodiments within the scope of thefollowing claims will be apparent to those skilled in the art.

1. A method of generating data traffic in a traffic generator, themethod comprising the steps of: generating a first type of traffic inaccordance with a given distribution; and generating a second type oftraffic different than the first type of traffic, the second type oftraffic comprising at least one traffic burst; wherein the traffic burstis generated based at least in part on an amount of the first type oftraffic generated over one or more time intervals.
 2. The method ofclaim 1 wherein the step of generating the second type of trafficfurther comprises accumulating traffic over one or more of the timeintervals for which the first type of traffic is generated, andgenerating the traffic burst based at least in part on the accumulatedtraffic.
 3. The method of claim 1 wherein the first type of trafficcomprises comparative traffic characteristic of non-burst traffic. 4.The method of claim 1 wherein the given distribution comprises a Poissondistribution.
 5. The method of claim 1 wherein the given distributioncomprises a Gaussian distribution.
 6. The method of claim 1 wherein thestep of generating the second type of traffic further comprises the stepof determining, for each of the one or more time intervals, if an amountof the traffic of the first type generated during that interval exceedsa comparison level, and if so adding an amount of compensatory trafficto a burst container having a capacity given by a burst size.
 7. Themethod of claim 6 wherein the traffic burst is generated when a totalamount of accumulated traffic in the burst container is greater than orequal to the burst size.
 8. The method of claim 6 wherein the burst sizeis determined as a function of a mean burst size and a correspondingvariation range.
 9. The method of claim 6 wherein the amount ofcompensatory traffic comprises an amount of traffic given by acompensatory-accumulation size.
 10. The method of claim 9 wherein thecompensatory-accumulation size is determined as a function of a meancompensatory-accumulation size and a corresponding variation range. 11.The method of claim 1 wherein the one or more time intervals eachcomprise sample slot times.
 12. The method of claim 1 wherein the stepof generating the second type of traffic further comprises generating aplurality of traffic bursts, wherein a given one of the traffic burstsis generated by: determining a current burst size and a currentcompensatory-accumulation size; creating an initially-empty burstcontainer having a capacity that is equal to the burst size; addingcompensatory traffic to the burst container whenever the total trafficof the first type generated within a given sample slot time is less thana comparison level, such that for each such addition of compensatorytraffic, a level of traffic in the burst container increases by thecompensatory-accumulation size; and generating the given traffic burstwhen the burst container level is greater than or equal to the burstsize.
 13. The method of claim 1 wherein the traffic of the second typecomprises a plurality of traffic bursts which are generated in a mannerwhich tends to compensate for temporary reductions in the amount oftraffic of the first type so as to substantially maintain a particularlevel of traffic flow.
 14. The method of claim 1 wherein the trafficgenerator comprises a hardware traffic generator.
 15. The method ofclaim 1 wherein the traffic generator comprises a software trafficgenerator.
 16. An apparatus for generating data traffic, the apparatuscomprising an information processing device having a processor and amemory, the information processing device implementing a trafficgenerator operative: to generate a first type of traffic in accordancewith a given distribution; and to generate a second type of trafficdifferent than the first type of traffic, the second type of trafficcomprising at least one traffic burst; wherein the traffic burst isgenerated based at least in part on an amount of the first type oftraffic generated over one or more time intervals.
 17. An article ofmanufacture comprising a storage medium containing one or more softwareprograms for use in generating data traffic in a traffic generator,wherein the one or more software programs when executed implement thesteps of: generating a first type of traffic in accordance with a givendistribution; and generating a second type of traffic different than thefirst type of traffic, the second type of traffic comprising at leastone traffic burst; wherein the traffic burst is generated based at leastin part on an amount of the first type of traffic generated over one ormore time intervals.